We evaluated our approach on several multi-label classification data sets available in the well-known MULAN repositoryhttp://mulan.sourceforge.net/datasets-mlc.html (see Section ), with a varying number of class labels, attributes and number of samples, and compared it with related multi-label concepts such as classifier chaining [1]} and one-versus-rest classification [2]}, both operating also in an incremental and evolving manner based on fuzzy classifier architecture. This provided us a fair comparison as to how the multi-output architecture and especially the incremental (locally) weighted and regularized correlation-based learning could improve the classification performance (see Section ). Furthermore, we applied our online active learning technique to check to what degree a reduction in the requested sample annotations impacts classification accuracy. A significant result was that a reduction down to 10% had a minimal effect on the accumulated (ahead prediction) accuracy trend lines in most data set cases. In the subsequent section, we describe the problem and the classifier architecture, which is followed by a comprehensive section about the methodological aspects of our approach.
